Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning.
Univariate and Multivariate Accelerated Spatial Modeling by Bayesian Predictive Stacking
This package provides the principal functions to perform accelerated modeling for univariate and multivariate spatial regressions. The package is used mostly within the novel working paper "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Luca Presicce and Sudipto Banerjee, 2024+)". To guarantee the reproducibility of scientific results, in the Bayesian-Transfer-Learning-for-GeoAI repository are also available all the scripts of code used for simulations, data analysis, and results presented in the Manuscript and its Supplemental material.
Roadmap
| Folder | Description |
|---|---|
R | contains funtions in R |
src | contains function in Rcpp/C++ |
Guided installation
Since the package is not already available on CRAN (already submitted, and hopefully soon available), we use the devtools R package to install. Then, check for its presence on your device, otherwise install it:
if (!require(devtools)) {
install.packages("devtools", dependencies = TRUE)
}
Once you have installed devtools, we can proceed. Let's install the spBPS package!
devtools::install_github("lucapresicce/spBPS")
Cool! You are ready to start, now you too could perform fast & feasible Bayesian geostatistical modeling!
Contacts
| Author | Luca Presicce ([email protected]) & Sudipto Banerjee ([email protected]) |
| Maintainer | Luca Presicce ([email protected]) |
| Reference | Luca Presicce and Sudipto Banerjee (2024+) "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" |